Exercise and activity devices that measure biometric and environmental data such as heart rate, speed, leg or arm turnover or stroke rate, altitude, temperature, heart rate variability, power, slope, distance per turnover, location, distance, time and other parameters currently exist. This data is displayed on a watch or device screen or spoken through headphones. These systems are measuring or data devices.
These devices are unable to clearly interpret collected data and provide solutions to improve a user's physical abilities in fitness and sports training. FIG. 1 demonstrates the difficulty in interpreting currently available recorded raw data that is provided to users for their own analysis. This means that once the activity data is collected, the user must have the relevant level of skill to analyse and interpret it and then decide upon the changes that they would make to their future exercise to optimize their time and effort during training and to maximize the improvements. This currently occurs post exercise but also in real time but in all cases requires someone skilled in the art to analyse the data.
In most cases, users ultimately do not want data from a measuring device which is the current paradigm, they want to know what was correct about what they did, what problems and solutions they need to work on and what to do next. They need someone or something to interpret the data and provide intelligent feedback.
Hundreds of millions of people around the world exercise ineffectively due to poor understanding of the appropriate strategies to maximizing fitness, sports performance and health improvements through activity. In a percentage of cases incorrect activity and exercise methods lead to needless and avoidable injury, illness and even death which is both unfortunate and costly.
Most people engage in exercise and activity without the presence of someone skilled in the art to guide them.
Should they exercise for longer? Should they do another hill? Should they do speed work or should they stop?
This is extremely important and relevant currently for creating safer, more effective activity and exercise.
One issue is to find a way to utilise sensors and algorithms to provide prescriptive plan changes and coaching advice to an unsupervised user engaged in exercise and activities.
This problem requires a method for understanding what the user is doing through contextualising and classifying different user activity into Activity Type segments and an ability to update these activities into the future and potentially provide advice that causes the user to make modifications to future activities.
There have been a number of attempts to correct this problem.
De Vries 2001 application EP1159989 disclosed a method, device and system for generating and/or adjusting a training schedule based on data or preferences that are input by a user or automatically from one or more sensors. It also discloses a server system for selecting a training schedule from a database or adjusting an earlier schedule in accordance with obtained parameters.
It does not disclose contextualisation of an activity segment or a classification of an activity within a period of different activities. It also does not disclose automated modifications of future activity segments within a period of different activities or providing coaching advice based on modifications to future activity.
In 2004 van Diemen disclosed in NL1027059 a method, device and system for generating and/or adjusting a training schedule.
Contextualisation of an activity segment or classification of an activity were not disclosed. Automated modifications of future activity segments and providing advice based on future activity segment modifications was also not disclosed.
Kurunmaki in U.S. Pat. No. 7,717,827 disclosed a method and system for determining and adapting a training plan for a user based on a training load for a user within a template and progress within a template. This was achieved with respect to a training load such that, over a time period, the user reaches a cumulative load target.
U.S. Pat. No. 7,717,827 did not disclose contextualisation of an activity segment or the classification of an activity and did not cover automated modifications of future activity segments or providing coaching advice based on modifications to future activity.
There have also been some attempts to solve parts of this problem but each method does not employ contextualisation of activity segments or the methodology of converting performance measures based on contextualised activity into modifications to an Activity Plan or advice on alterations in future behaviour for activity.
An example is that the state of the art has developed such that some level of classification is possible known as Training Zones. There is a significant problem with this current activity measurement paradigm in which coaching information and activity plan updates are based on a single parameter.
To explain, Training Zones involve a single measured parameter like heart rate, speed, power or limb turnover and are used where the user must maintain a prescribed level like heart rate within a band or zone like 165 to 175 heart beats per minute. (see FIG. 2) This is measured over time or distance. The Heart Rate Training Zones in FIG. 2 do not make anaylsis of the data any easier.
There are systems currently available where Training Zones can be classified and data can be recorded if it conforms to the prescribed parameter threshold as a classified ‘Training Zone’ measuring ‘Time in Zone’.
BUT multiple parameters are not recorded in concert to classify an Activity Type and they are not used for the purposes of automated interpretation and coaching prescription and modification of a plan.
Training Zones are given names to indicate what they might be like so titles like ‘fat loss zone’ or ‘E2 zone’, ‘Maximal zone’, ‘Race Pace zone’ are used. While these zones are excellent for training, like “go into the Easy zone” or “go into the Fast zone” they are not very good for data analysis. This is because there are a number of assumptions made when using Training Zones.
It is assumed that when you should go into the Easy Training Zone that you're not going to be climbing a steep hill. Going into a Speed Training Zone assumes you are on the flat and not running up that steep hill.
These scenarios are sufficient if there is no need for accurate automated analysis but as soon as there is a requirement to measure accurately what the user is doing, other parameters are necessary.
Questions arise like was the ‘Speed Training Zone’ training conducted on the flat because if it was not on the flat, it shouldn't be classed as Speed Training because comparing Speed Training on the flat with Speed Training up a hill will lead to large errors in automated coaching feedback.
Training Zones are mono parameter classifications and therefore do not contextualise activity and any advice provided or modifications to an Activity Plan are subject to large errors.
The state of the art also employs a number of performance measures like EPOC, Training Effectiveness, Acute Training Load, Chronic Training Load, Training Stress Score, Heart Rate Variability, VO2max and exercise economy but each does not have a method for modifying Activity Type segments in planned future Activity Sessions or does not provide advice that modifies future behaviour for activity based on automated analysis of current Activity Type segments.
The state of the art also features some measures around compliance but once again these do not feature measuring Activity Type segments.
In some cases, coaches and trainers manually use multiple zones in concert with each other to describe how a user should train but do not use multiple parameters in concert to define the classification of an Activity Type to automatically detect data that conforms to particular parameter zone combinations for automatic classification, interpretation and for providing coaching prescription.
It is an object of the present invention to provide a method and system for accurate automated activity interpretation and prescription which can be in the form of advice or modification of an activity session or long term Activity Plan in real time or post activity, or to at least provide the public with a useful choice.
Multi parameter contextualisation for classifying activities is used for updating future classified Activity Type segments within an Activity session or within a long term Activity Plan and to provide advice on a user's behavioural modifications to future activity.